221 citations · 507 across the 42 of their papers we have counts for
10 papers · 1 filter
Unbiased Supervised Contrastive Learning
Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione +2
Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distributio…
Compressing Explicit Voxel Grid Representations: fast NeRFs become also small
Chenxi Lola Deng, Enzo Tartaglione
NeRFs have revolutionized the world of per-scene radiance field reconstruction because of their intrinsic compactness. One of the main limitations of NeRFs is their slow rendering…
Packed-Ensembles for Efficient Uncertainty Estimation
Olivier Laurent, Adrien Lafage, Enzo Tartaglione +4
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detec…
Information Removal at the bottleneck in Deep Neural Networks
Enzo Tartaglione
Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. Commonly, leveraging over the availability of "big data", deep neural networks are…
UniToBrain dataset: a Brain Perfusion Dataset
Daniele Perlo, Enzo Tartaglione, Umberto Gava +3
The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus of contrast solution through the brain on a pixel-by-pixel basis. The objective is to draw "perfusion…
Disentangling private classes through regularization
Enzo Tartaglione, Francesca Gennari, Marco Grangetto
Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. However, little attention has been devoted to connected legal aspects. In 2016, th…
To update or not to update? Neurons at equilibrium in deep models
Andrea Bragagnolo, Enzo Tartaglione, Marco Grangetto
Recent advances in deep learning optimization showed that, with some a-posteriori information on fully-trained models, it is possible to match the same performance by simply traini…
Unsupervised Learning of Unbiased Visual Representations
Carlo Alberto Barbano, Enzo Tartaglione, Marco Grangetto
Deep neural networks often struggle to learn robust representations in the presence of dataset biases, leading to suboptimal generalization on unbiased datasets. This limitation ar…
Capsule Networks Do Not Need to Model Everything
Riccardo Renzulli, Enzo Tartaglione, Marco Grangetto
Capsule networks are biologically inspired neural networks that group neurons into vectors called capsules, each explicitly representing an object or one of its parts. The routing…
The rise of the lottery heroes: why zero-shot pruning is hard
Enzo Tartaglione
Recent advances in deep learning optimization showed that just a subset of parameters are really necessary to successfully train a model. Potentially, such a discovery has broad im…